Data come from a survey of alcohol consumption and socioeconomic status. Data are contained in ALCOHOL.REC (713 records of 5 bytes each), with variables coded as follows:
Variable Name | Type | Description and codes |
ALCS | ## | Alcohol consumption score. Codes are as follows: 00 = non-drinker 01 = 1 drink per week 02 = 1-2 drinks per week 03 = 2 drinks per week 04 = 2-3 drinks per week 05 = 3 drinks per week 06 = 3-4 drinks per week 07 = 4 drinks per week 08 = 4-5 drinks per week 09 = 5 drinks per week 10 = 5-6 drinks per week 11 = 6 drinks per week 12 = 7-11 drinks per week 13 = 12+ drinks per week |
AGE | ## | Age (in years) |
INC | # | Income level: 1 = low, 5 = high |
Before analyzing alcohol consumption scores by group , perform a descriptive analysis of ALCS for all groups combined (command: MEANS ALCS). Report the mean, standard deviation, sample size, and a five-point summary of the outcome (minimum, Q2, median, Q3, and maximum). Also, produce a histogram of the variable (HISTOGRAM ALCS), and describe (in words) the distribution's shape and location.
Categorize age into 3 age-class intervals as follows: 20- to 29-year-olds, 30- to 42-year-olds, and 43+ year-olds. This can be
accomplished with the following commands:
EPI6> DEFINE AGEGROUP #
EPI6> IF AGE <= 29 THEN AGEGROUP = 1
EPI6> IF AGE >= 30 AND AGE <= 42 THEN AGEGROUP = 2
EPI6> IF AGE >= 43 THEN AGEGROUP = 3
EPI6> MEANS ALCS AGEGROUP /N
Perform an analysis similar to the one described in Part B above, now directing your analysis toward alcohol consumption scores by age group. Label your analysis 1 - 7, as above.
Fifteen white-footed deer mice are randomly assigned to one of three groups. Group A receives a diet of standard mouse food, Group B receives a diet of junk food, and Group C receives a diet of health food. Data are:
REC DIET WTGAIN
--- ---- ------
1 A 11.8
2 A 12.0
3 A 10.7
4 A 9.1
5 A 12.1
6 B 13.6
7 B 14.4
8 B 12.8
9 B 13.0
10 B 13.4
11 C 9.2
12 C 9.6
13 C 8.6
14 C 8.5
15 C 9.8
Create an Epi Info data set with these data and then perform an analysis similar to the ones described above.
A chicken pathologist believes that testosterone levels may differ by rooster strain. To test her hypothesis, testosterone
levels are measured in 3 strains of roosters. Data are as follows:
REC TESTOSTERO STRAIN
--- ---------- ------
1 439 A
2 568 A
3 134 A
4 897 A
5 229 A
6 329 A
7 103 B
8 115 B
9 98 B
10 126 B
11 115 B
12 120 B
13 107 C
14 99 C
15 102 C
16 105 C
17 89 C
18 110 C
(A) Create an Epi Info data file with these data
(B) Compute summary statistics by rooster strain.
(C) Test for inequality of variances.
(D) Test for inequality of means.
(E) Suppose we now want to design a new experiment to test whether average testosterone levels difer in Strain B and
Strain C . Let us start with the following simplifying assumptions: k = 2 (Strain B vs. Strain C), n (per group) = 10, within
group variance = 100, and = .05. What is the power of this new study to find a minimal detectable difference of 10 units? 15
units? 20 units?
(F) Let us now turn the question on its head by asking how many roosters are needed in each group to find a difference of
10 units. We want 80% power.
Assume alpha = .05, s-squared = 100, n = 10, and k = 2.